This specification relates to autonomous vehicles.
Autonomous vehicles include self-driving cars, boats, and aircraft. Autonomous vehicles use a variety of on-board sensors and computer systems to detect nearby objects and use such detections to make control and navigation decisions.
Some autonomous vehicles have computer systems that implement neural networks for various planning and detection tasks. Neural networks, or for brevity, networks, are machine learning models that employ multiple layers of operations to predict one or more outputs from one or more inputs. Neural networks typically include one or more hidden layers situated between an input layer and an output layer. The output of each layer is used as input to another layer in the network, e.g., the next hidden layer or the output layer. As an example, a neural network can be used to determine that an image captured by an on-board camera is likely to contain an image of a nearby car.
Reinforcement learning is an area of machine learning that involves training software agents to interact with an environment. Agents receive observations that each characterize the current state of the environment, and in response, perform actions that are designed to maximize a cumulative reward. Some reinforcement learning agents use neural networks to select the action to be performed in response to receiving a given observation.